Towards the Selection of Superior Sesame Lines Based on Genetic and Phenotypic Characterisation for Uganda
Why this work is in the frame
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Bibliographic record
Abstract
Understanding agricultural biodiversity is critical to formulate breeding strategies for crop improvement and it impacts both, conservation and collection activities. Especially germplasm collections serve as valuable resources, thus, their adequate characterisation is of utmost importance. Although Uganda ranks seventh in African sesame production, meagre research was conducted to determine the current genetic diversity among its germplasm. Therefore, in the present study part of the sesame germplasm conserved at the National Semi-Arid Resources Research Institute (NaSARRI) in Uganda focusing on 85 established lines was genetically and phenotypically characterised. Population genetic and structure analyses revealed rather a low extend of genetic diversity (expected heterozygosity [HE], or gene diversity [D]) ranging from 0 to 0.38 per entry, but a high extend of admixture within and between entries. This decrease of heterozygosity is supported by a fixation index (FST) of 0.530, indicating a medium genetic differentiation among entries. The analysis of quantitative and qualitative agromorphological traits revealed a great inter-trait variability among the entries and further indicated a certain conservation of some of the traits reflecting the geographic origin of the analysed entries. Based on both, the genetic and phenotypic characterisation, a selection of 26 superior entries is proposed, which may form a valuable basis both for farmers and breeders.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it